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Prediction of dust fall concentrations in urban atmospheric environment through support vector regression

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Abstract

Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters (insensitive loss function ɛ, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ) are 0.001, 0.5, and 2 000, respectively.

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Authors and Affiliations

  1. College of Architecture, Hunan University, Changsha, 410082, China

    Sheng Jiao  (焦胜) & Qing Gao  (高青)

  2. College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China

    Guang-ming Zeng  (曾光明)

  3. Faculty of Engineering, Architecture and Science, Ryerson University, Toronto, Ontario, M5B 2K3, Canada

    Li He  (何理)

  4. Faculty of Engineering, University of Regina, Regina, Sask, S4S 0A2, Canada

    Guo-he Huang  (黄国和) & Hong-wei Lu  (卢宏玮)

Authors
  1. Sheng Jiao  (焦胜)
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  2. Guang-ming Zeng  (曾光明)
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  3. Li He  (何理)
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  4. Guo-he Huang  (黄国和)
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  5. Hong-wei Lu  (卢宏玮)
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  6. Qing Gao  (高青)
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Corresponding author

Correspondence to Sheng Jiao  (焦胜).

Additional information

Foundation item: Projects(2007JT3018, 2008JT1013, 2009FJ4056) supported by the Key Project in Hunan Science and Technology Program, China; Project(20090161120014) supported by the New Teachers Sustentation Fund in Doctoral Program, Ministry of Education, China

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Jiao, S., Zeng, Gm., He, L. et al. Prediction of dust fall concentrations in urban atmospheric environment through support vector regression. J. Cent. South Univ. Technol. 17, 307–315 (2010). https://doi.org/10.1007/s11771-010-0047-x

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  • DOI: https://doi.org/10.1007/s11771-010-0047-x

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